// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_GENERAL_PRODUCT_H
#define EIGEN_GENERAL_PRODUCT_H

namespace Eigen {

enum
{
	Large = 2,
	Small = 3
};

// Define the threshold value to fallback from the generic matrix-matrix product
// implementation (heavy) to the lightweight coeff-based product one.
// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
// in products/GeneralMatrixMatrix.h for more details.
// TODO This threshold should also be used in the compile-time selector below.
#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
// This default value has been obtained on a Haswell architecture.
#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
#endif

namespace internal {

template<int Rows, int Cols, int Depth>
struct product_type_selector;

template<int Size, int MaxSize>
struct product_size_category
{
	enum
	{
#ifndef EIGEN_GPU_COMPILE_PHASE
		is_large = MaxSize == Dynamic || Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
				   (Size == Dynamic && MaxSize >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
#else
		is_large = 0,
#endif
		value = is_large	? Large
				: Size == 1 ? 1
							: Small
	};
};

template<typename Lhs, typename Rhs>
struct product_type
{
	typedef typename remove_all<Lhs>::type _Lhs;
	typedef typename remove_all<Rhs>::type _Rhs;
	enum
	{
		MaxRows = traits<_Lhs>::MaxRowsAtCompileTime,
		Rows = traits<_Lhs>::RowsAtCompileTime,
		MaxCols = traits<_Rhs>::MaxColsAtCompileTime,
		Cols = traits<_Rhs>::ColsAtCompileTime,
		MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::MaxColsAtCompileTime, traits<_Rhs>::MaxRowsAtCompileTime),
		Depth = EIGEN_SIZE_MIN_PREFER_FIXED(traits<_Lhs>::ColsAtCompileTime, traits<_Rhs>::RowsAtCompileTime)
	};

	// the splitting into different lines of code here, introducing the _select enums and the typedef below,
	// is to work around an internal compiler error with gcc 4.1 and 4.2.
  private:
	enum
	{
		rows_select = product_size_category<Rows, MaxRows>::value,
		cols_select = product_size_category<Cols, MaxCols>::value,
		depth_select = product_size_category<Depth, MaxDepth>::value
	};
	typedef product_type_selector<rows_select, cols_select, depth_select> selector;

  public:
	enum
	{
		value = selector::ret,
		ret = selector::ret
	};
#ifdef EIGEN_DEBUG_PRODUCT
	static void debug()
	{
		EIGEN_DEBUG_VAR(Rows);
		EIGEN_DEBUG_VAR(Cols);
		EIGEN_DEBUG_VAR(Depth);
		EIGEN_DEBUG_VAR(rows_select);
		EIGEN_DEBUG_VAR(cols_select);
		EIGEN_DEBUG_VAR(depth_select);
		EIGEN_DEBUG_VAR(value);
	}
#endif
};

/* The following allows to select the kind of product at compile time
 * based on the three dimensions of the product.
 * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
// FIXME I'm not sure the current mapping is the ideal one.
template<int M, int N>
struct product_type_selector<M, N, 1>
{
	enum
	{
		ret = OuterProduct
	};
};
template<int M>
struct product_type_selector<M, 1, 1>
{
	enum
	{
		ret = LazyCoeffBasedProductMode
	};
};
template<int N>
struct product_type_selector<1, N, 1>
{
	enum
	{
		ret = LazyCoeffBasedProductMode
	};
};
template<int Depth>
struct product_type_selector<1, 1, Depth>
{
	enum
	{
		ret = InnerProduct
	};
};
template<>
struct product_type_selector<1, 1, 1>
{
	enum
	{
		ret = InnerProduct
	};
};
template<>
struct product_type_selector<Small, 1, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<1, Small, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Small, Small, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Small, Small, 1>
{
	enum
	{
		ret = LazyCoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Small, Large, 1>
{
	enum
	{
		ret = LazyCoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Large, Small, 1>
{
	enum
	{
		ret = LazyCoeffBasedProductMode
	};
};
template<>
struct product_type_selector<1, Large, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<1, Large, Large>
{
	enum
	{
		ret = GemvProduct
	};
};
template<>
struct product_type_selector<1, Small, Large>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Large, 1, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Large, 1, Large>
{
	enum
	{
		ret = GemvProduct
	};
};
template<>
struct product_type_selector<Small, 1, Large>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Small, Small, Large>
{
	enum
	{
		ret = GemmProduct
	};
};
template<>
struct product_type_selector<Large, Small, Large>
{
	enum
	{
		ret = GemmProduct
	};
};
template<>
struct product_type_selector<Small, Large, Large>
{
	enum
	{
		ret = GemmProduct
	};
};
template<>
struct product_type_selector<Large, Large, Large>
{
	enum
	{
		ret = GemmProduct
	};
};
template<>
struct product_type_selector<Large, Small, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Small, Large, Small>
{
	enum
	{
		ret = CoeffBasedProductMode
	};
};
template<>
struct product_type_selector<Large, Large, Small>
{
	enum
	{
		ret = GemmProduct
	};
};

} // end namespace internal

/***********************************************************************
 *  Implementation of Inner Vector Vector Product
 ***********************************************************************/

// FIXME : maybe the "inner product" could return a Scalar
// instead of a 1x1 matrix ??
// Pro: more natural for the user
// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
// product ends up to a row-vector times col-vector product... To tackle this use
// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);

/***********************************************************************
 *  Implementation of Outer Vector Vector Product
 ***********************************************************************/

/***********************************************************************
 *  Implementation of General Matrix Vector Product
 ***********************************************************************/

/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
 *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
 *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
 *   3 - all other cases are handled using a simple loop along the outer-storage direction.
 *  Therefore we need a lower level meta selector.
 *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
 */
namespace internal {

template<int Side, int StorageOrder, bool BlasCompatible>
struct gemv_dense_selector;

} // end namespace internal

namespace internal {

template<typename Scalar, int Size, int MaxSize, bool Cond>
struct gemv_static_vector_if;

template<typename Scalar, int Size, int MaxSize>
struct gemv_static_vector_if<Scalar, Size, MaxSize, false>
{
	EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data()
	{
		eigen_internal_assert(false && "should never be called");
		return 0;
	}
};

template<typename Scalar, int Size>
struct gemv_static_vector_if<Scalar, Size, Dynamic, true>
{
	EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; }
};

template<typename Scalar, int Size, int MaxSize>
struct gemv_static_vector_if<Scalar, Size, MaxSize, true>
{
	enum
	{
		ForceAlignment = internal::packet_traits<Scalar>::Vectorizable,
		PacketSize = internal::packet_traits<Scalar>::size
	};
#if EIGEN_MAX_STATIC_ALIGN_BYTES != 0
	internal::
		plain_array<Scalar, EIGEN_SIZE_MIN_PREFER_FIXED(Size, MaxSize), 0, EIGEN_PLAIN_ENUM_MIN(AlignedMax, PacketSize)>
			m_data;
	EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
#else
	// Some architectures cannot align on the stack,
	// => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
	internal::plain_array<Scalar,
						  EIGEN_SIZE_MIN_PREFER_FIXED(Size, MaxSize) + (ForceAlignment ? EIGEN_MAX_ALIGN_BYTES : 0),
						  0>
		m_data;
	EIGEN_STRONG_INLINE Scalar* data()
	{
		return ForceAlignment ? reinterpret_cast<Scalar*>(
									(internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES - 1))) +
									EIGEN_MAX_ALIGN_BYTES)
							  : m_data.array;
	}
#endif
};

// The vector is on the left => transposition
template<int StorageOrder, bool BlasCompatible>
struct gemv_dense_selector<OnTheLeft, StorageOrder, BlasCompatible>
{
	template<typename Lhs, typename Rhs, typename Dest>
	static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha)
	{
		Transpose<Dest> destT(dest);
		enum
		{
			OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor
		};
		gemv_dense_selector<OnTheRight, OtherStorageOrder, BlasCompatible>::run(
			rhs.transpose(), lhs.transpose(), destT, alpha);
	}
};

template<>
struct gemv_dense_selector<OnTheRight, ColMajor, true>
{
	template<typename Lhs, typename Rhs, typename Dest>
	static inline void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha)
	{
		typedef typename Lhs::Scalar LhsScalar;
		typedef typename Rhs::Scalar RhsScalar;
		typedef typename Dest::Scalar ResScalar;
		typedef typename Dest::RealScalar RealScalar;

		typedef internal::blas_traits<Lhs> LhsBlasTraits;
		typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
		typedef internal::blas_traits<Rhs> RhsBlasTraits;
		typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;

		typedef Map<Matrix<ResScalar, Dynamic, 1>,
					EIGEN_PLAIN_ENUM_MIN(AlignedMax, internal::packet_traits<ResScalar>::size)>
			MappedDest;

		ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
		ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);

		ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);

		// make sure Dest is a compile-time vector type (bug 1166)
		typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest;

		enum
		{
			// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
			// on, the other hand it is good for the cache to pack the vector anyways...
			EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime == 1),
			ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
			MightCannotUseDest =
				((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime != 0)
		};

		typedef const_blas_data_mapper<LhsScalar, Index, ColMajor> LhsMapper;
		typedef const_blas_data_mapper<RhsScalar, Index, RowMajor> RhsMapper;
		RhsScalar compatibleAlpha = get_factor<ResScalar, RhsScalar>::run(actualAlpha);

		if (!MightCannotUseDest) {
			// shortcut if we are sure to be able to use dest directly,
			// this ease the compiler to generate cleaner and more optimzized code for most common cases
			general_matrix_vector_product<Index,
										  LhsScalar,
										  LhsMapper,
										  ColMajor,
										  LhsBlasTraits::NeedToConjugate,
										  RhsScalar,
										  RhsMapper,
										  RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(),
																			   actualLhs.cols(),
																			   LhsMapper(actualLhs.data(),
																						 actualLhs.outerStride()),
																			   RhsMapper(actualRhs.data(),
																						 actualRhs.innerStride()),
																			   dest.data(),
																			   1,
																			   compatibleAlpha);
		} else {
			gemv_static_vector_if<ResScalar,
								  ActualDest::SizeAtCompileTime,
								  ActualDest::MaxSizeAtCompileTime,
								  MightCannotUseDest>
				static_dest;

			const bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha) == RealScalar(0));
			const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;

			ei_declare_aligned_stack_constructed_variable(
				ResScalar, actualDestPtr, dest.size(), evalToDest ? dest.data() : static_dest.data());

			if (!evalToDest) {
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
				Index size = dest.size();
				EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
				if (!alphaIsCompatible) {
					MappedDest(actualDestPtr, dest.size()).setZero();
					compatibleAlpha = RhsScalar(1);
				} else
					MappedDest(actualDestPtr, dest.size()) = dest;
			}

			general_matrix_vector_product<Index,
										  LhsScalar,
										  LhsMapper,
										  ColMajor,
										  LhsBlasTraits::NeedToConjugate,
										  RhsScalar,
										  RhsMapper,
										  RhsBlasTraits::NeedToConjugate>::run(actualLhs.rows(),
																			   actualLhs.cols(),
																			   LhsMapper(actualLhs.data(),
																						 actualLhs.outerStride()),
																			   RhsMapper(actualRhs.data(),
																						 actualRhs.innerStride()),
																			   actualDestPtr,
																			   1,
																			   compatibleAlpha);

			if (!evalToDest) {
				if (!alphaIsCompatible)
					dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
				else
					dest = MappedDest(actualDestPtr, dest.size());
			}
		}
	}
};

template<>
struct gemv_dense_selector<OnTheRight, RowMajor, true>
{
	template<typename Lhs, typename Rhs, typename Dest>
	static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha)
	{
		typedef typename Lhs::Scalar LhsScalar;
		typedef typename Rhs::Scalar RhsScalar;
		typedef typename Dest::Scalar ResScalar;

		typedef internal::blas_traits<Lhs> LhsBlasTraits;
		typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
		typedef internal::blas_traits<Rhs> RhsBlasTraits;
		typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
		typedef typename internal::remove_all<ActualRhsType>::type ActualRhsTypeCleaned;

		typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(lhs);
		typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(rhs);

		ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);

		enum
		{
			// FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
			// on, the other hand it is good for the cache to pack the vector anyways...
			DirectlyUseRhs =
				ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime == 0
		};

		gemv_static_vector_if<RhsScalar,
							  ActualRhsTypeCleaned::SizeAtCompileTime,
							  ActualRhsTypeCleaned::MaxSizeAtCompileTime,
							  !DirectlyUseRhs>
			static_rhs;

		ei_declare_aligned_stack_constructed_variable(RhsScalar,
													  actualRhsPtr,
													  actualRhs.size(),
													  DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data())
																	 : static_rhs.data());

		if (!DirectlyUseRhs) {
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
			Index size = actualRhs.size();
			EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
			Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
		}

		typedef const_blas_data_mapper<LhsScalar, Index, RowMajor> LhsMapper;
		typedef const_blas_data_mapper<RhsScalar, Index, ColMajor> RhsMapper;
		general_matrix_vector_product<Index,
									  LhsScalar,
									  LhsMapper,
									  RowMajor,
									  LhsBlasTraits::NeedToConjugate,
									  RhsScalar,
									  RhsMapper,
									  RhsBlasTraits::NeedToConjugate>::
			run(actualLhs.rows(),
				actualLhs.cols(),
				LhsMapper(actualLhs.data(), actualLhs.outerStride()),
				RhsMapper(actualRhsPtr, 1),
				dest.data(),
				dest.col(0).innerStride(), // NOTE  if dest is not a vector at compile-time, then dest.innerStride()
										   // might be wrong. (bug 1166)
				actualAlpha);
	}
};

template<>
struct gemv_dense_selector<OnTheRight, ColMajor, false>
{
	template<typename Lhs, typename Rhs, typename Dest>
	static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha)
	{
		EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
							EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
		// TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory,
		// otherwise use a temp
		typename nested_eval<Rhs, 1>::type actual_rhs(rhs);
		const Index size = rhs.rows();
		for (Index k = 0; k < size; ++k)
			dest += (alpha * actual_rhs.coeff(k)) * lhs.col(k);
	}
};

template<>
struct gemv_dense_selector<OnTheRight, RowMajor, false>
{
	template<typename Lhs, typename Rhs, typename Dest>
	static void run(const Lhs& lhs, const Rhs& rhs, Dest& dest, const typename Dest::Scalar& alpha)
	{
		EIGEN_STATIC_ASSERT((!nested_eval<Lhs, 1>::Evaluate),
							EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
		typename nested_eval<Rhs, Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
		const Index rows = dest.rows();
		for (Index i = 0; i < rows; ++i)
			dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
	}
};

} // end namespace internal

/***************************************************************************
 * Implementation of matrix base methods
 ***************************************************************************/

/** \returns the matrix product of \c *this and \a other.
 *
 * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
 *
 * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
 */
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived>
MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived>& other) const
{
	// A note regarding the function declaration: In MSVC, this function will sometimes
	// not be inlined since DenseStorage is an unwindable object for dynamic
	// matrices and product types are holding a member to store the result.
	// Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
	enum
	{
		ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
						 int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
		AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
		SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
	};
	// note to the lost user:
	//    * for a dot product use: v1.dot(v2)
	//    * for a coeff-wise product use: v1.cwiseProduct(v2)
	EIGEN_STATIC_ASSERT(
		ProductIsValid || !(AreVectors && SameSizes),
		INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
	EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
						INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
	EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
#ifdef EIGEN_DEBUG_PRODUCT
	internal::product_type<Derived, OtherDerived>::debug();
#endif

	return Product<Derived, OtherDerived>(derived(), other.derived());
}

/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
 *
 * The returned product will behave like any other expressions: the coefficients of the product will be
 * computed once at a time as requested. This might be useful in some extremely rare cases when only
 * a small and no coherent fraction of the result's coefficients have to be computed.
 *
 * \warning This version of the matrix product can be much much slower. So use it only if you know
 * what you are doing and that you measured a true speed improvement.
 *
 * \sa operator*(const MatrixBase&)
 */
template<typename Derived>
template<typename OtherDerived>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Product<Derived, OtherDerived, LazyProduct>
MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived>& other) const
{
	enum
	{
		ProductIsValid = Derived::ColsAtCompileTime == Dynamic || OtherDerived::RowsAtCompileTime == Dynamic ||
						 int(Derived::ColsAtCompileTime) == int(OtherDerived::RowsAtCompileTime),
		AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
		SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived, OtherDerived)
	};
	// note to the lost user:
	//    * for a dot product use: v1.dot(v2)
	//    * for a coeff-wise product use: v1.cwiseProduct(v2)
	EIGEN_STATIC_ASSERT(
		ProductIsValid || !(AreVectors && SameSizes),
		INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
	EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
						INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
	EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)

	return Product<Derived, OtherDerived, LazyProduct>(derived(), other.derived());
}

} // end namespace Eigen

#endif // EIGEN_PRODUCT_H
